Last decade has witnessed the thriving development of mobile communication industry, not limited to the proliferation of mobiles, but also the various computing-intensive mobile applications. The question of how to address the tremendous computation has incurred the promising technology of Mobile Cloud Computing (MCC) <9>, which, alternatively, offloads the computation from the energy-hungry mobiles to a powerful cloud.
The essence of MCC, shifting the energy consumption from the local computing to offloading, leads to the joint design of wireless communication and mobile computing techniques, to enhance the computation capacity and prolong the lives of mobiles. For local computing, energy-efficient mobile control have been developed for a long time, such as the multiple task scheduling <8>, dynamic voltage scaling <4> and power management<2>. For offloading, diversified mechanisms have been investigated intensively [1, 6, 7, 11, 12]. For a single-task application, the offloading decision was obtained in <12> after optimizing the control methods of local computing and offloading. In <11>, one collaborate task execution was proposed to minimize the energy consumption of multiple-task applications. Furthermore, <6> derived the optimal application partitioning to achieve the maximum throughput of data stream. Nevertheless, all of above work focused on the single-mobile offloading and the extensions of them to the multiple-mobiles scenario are unknown. Afterwards, more attention was paid to the multiuser mobile cloud computing diagram. In <1>, the transmission power and CPU cycles were controlled for minimizing the power consumption of mobiles. However, the scheduling delay has not been considered, which refers to the waiting time due to the highly-loaded cloud and affected by the aggregated amount of offloaded data in the cloud. In another work <7>, the scheduling delay was concerned for minimizing the average completion time for multiuser computation partitioning, however, that lacks the consideration for energy consumption. The current work optimizes the offloading policy in the multiuser MCC system for the minimum energy consumption, accounting for the individual delay constraint.
In this paper, we make attempt to characterize the offloading policy in the multiuser MCC system. The contributions of this paper are summarized as follows. First, assuming the deployment of TDMA, for each mobile, both the offloaded data size and offloading time duration should to optimized for the minimum weighted energy consumption under the individual delay constraint. The formulated convex problem is solved by the proposed two-stage method that is of low complexity and useful insights. Second, for the strong cloud with huge computation capacity, instead of iteratively solving KKT (Karush Kuhn Tucker) conditions, the optimal offloading policy is shown to be priority-and-threshold based where mobiles with high offloading priority tend to perform full offloading. Moreover, special cases with respect to (with respect to) channel gain and local computing power are analyzed and the greedy approaches are shown to be optimal. Last, the low-complexity suboptimal method for the critical cloud is proposed and verified to be close-to-optimal by simulations.